In many enterprise environments, data is generated and contained in various systems within the enterprise. However, in order to enable analysis, management, and planning of the data, the enterprise must combine the data into cohesive models, in a staging area, where the enterprise can validate, cleanse, correlate, and format the data, in order to pass it on to downstream consumers, such as, for example, data warehouses, planning engines, and the like.
Traditionally, the enterprise implemented a process based on a project by project basis, that is, a process that utilized a custom approach to create staging models and tables, load data using Extract, Transform, and Load (ETL) tools, and write custom scripts in order to validate and cleanse this data. However, this traditional process has proved disadvantageous, since, for example, the traditional ETL tools are limited in their capabilities and the custom scripts are seldom reusable and are typically difficult to maintain. The limited capabilities of ETL tools and the inability to reuse custom scripts are undesirable.